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该论文提出 BRaVeS（DNRS）框架，通过不变锚点、深度感知注意力与 Lyapunov 共识机制约束智能体推理的自主性，防止认知漂移，并在工业控制仿真中验证了有限步收敛与零安全违规。
The paper proposes RegLLM, a diagnostic harness for evaluating bounded autonomy in regulated agentic AI workflows, instrumenting six trustworthiness signals with constitutional rewards, escalation labels, and runtime governance. Small-scale Qwen2.5-3B pilots reveal that configuration variance can overwhelm apparent tuning effects on bounded-autonomy metrics, motivating larger evaluation sets and repeated runs.